It is important to consider that a caveat of our evaluation methodology is that poor explanation methods can be identified, but it cannot prove that an explainer is faithful for all models.
Thus, this framework can be seen as a benchmark and a testing ground for explainer quality.
Emphasizing some of the arguments made in [1]}, [2]}, our framework can alleviate some of the financial and other resource burdens by filtering out explainers, e.g., before a user study, that do not meet satisfactory marks.
Moreover, this filtering of explainers can reduce liabilities and aid in meeting AI regulations.
